ICTACT Journal on Soft Computing

S. K. Jayanthi ¹, S. Sasikala ²

Affiliations
1 Department of Computer Science, Vellalar College for Women, IN
2 Department of Computer Science, Hindusthan College of Arts and Science, IN

Abstract

Spamdexing is the art of black hat SEO. Features which are more influential for high rank and visibility are manipulated for the SEO task. The motivation behind the work is utilizing the state of the art Website optimization features to enhance the performance of spamdexing detection. Features which play a focal role in current SEO strategies show a significant deviation for spam and non-spam samples. This paper proposes 44 features named as NLSDF (New Link Spamdexing Detection Features). Social media creates an impact in search engine results ranking. Features pertaining to the social media were incorporated with the NLSDF features to boost the recital of the spamdexing classification. The NLSDF features with 44 attributes along with 5 social media features boost the classification performance of the WEBSPAM-UK 2007 dataset. The one tailed paired t-test with 95% confidence, performed on the AUC values of the learning models shows significance of the NLSDF.

Keywords

Web Spam, Search Engine, SVM, Decision Table, HMM.

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S. K. Jayanthi ¹, S. Sasikala ²

Affiliations
1 Department of Computer Science, Vellalar College for Women, IN
2 Department of Computer Science, K.S.R College of Arts and Science, IN

Abstract

Search engines are the doorsteps for retrieving required information from the web. Web spam is a bad method for improving the ranking and visibility of the web pages in search engine results. This paper addresses the problem of the link spam classification through the features of the web sites. Link related features retrieved from the website are used to discriminate the spam and non-spam sites. AIS inspired algorithms are applied for the dataset and results are evaluated. Artificial immune systems are machine learning systems inspired by the principles of the natural immunology. It comprises of supervised learning schemes which can be adapted for the wide range of the classification problems.UK- WEBSPAM-2007 Dataset [8] is used for the experiments. WEKA [9] is used to simulate the classifiers. Artificial Immune Recognition algorithm seems to perform well than the other classes. Best classification accuracy attained is 98.89 by AIRS1 Algorithm. This seems to be good when comparing with the other classifiers accuracy available on the existing literature.

Keywords

Web Spam, Search Engine, TPP, FCA, AIRS.

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S. K. Jayanthi ¹, S. Sasikala ²

Affiliations
1 Department of Computer Science, Vellalar College for Women, IN
2 Department of Computer Science, KSR College of Arts and Science, IN

Abstract

Search Engines are used for retrieving the information from the web. Most of the times, the importance is laid on top 10 results sometimes it may shrink as top 5, because of the time constraint and reliability on the search engines. Users believe that top 10 or 5 of total results are more relevant. Here comes the problem of spamdexing. It is a method to deceive the search result quality. Falsified metrics such as inserting enormous amount of keywords or links in website may take that website to the top 10 or 5 positions. This paper proposes a classifier based on the Reptree (Regression tree representative). As an initial step Link-based features such as neighbors, pagerank, truncated pagerank, trustrank and assortativity related attributes are inferred. Based on this features, tree is constructed. The tree uses the feature inference to differentiate spam sites from legitimate sites. WEBSPAM-UK-2007 dataset is taken as a base. It is preprocessed and converted into five datasets FEATA, FEATB, FEATC, FEATD and FEATE. Only link based features are taken for experiments. This paper focus on link spam alone. Finally a representative tree is created which will more precisely classify the web spam entries. Results are given. Regression tree classification seems to perform well as shown through experiments.

Keywords

Web Link Spam, Classification, Reptree, Decision Tree, Search Engine.

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S. Balaji ¹, S. Sasikala ²

Affiliations
1 Department of Computer Science, Sengunthar College of Engineering, IN
2 Department of Computer Science, K.S.R College of Arts and Science, IN

Abstract

Web logs are a young and dynamic media type. Due to the intrinsic relationship among Web objects and the deficiency of a uniform schema of web documents, Web community mining has become significant area for Web data management and analysis. The research of Web communities extents a number of research domains. In this paper an ontological model has been present with some recent studies on this topic, which cover finding relevant Web pages based on linkage information, discovering user access patterns through analyzing Web log files from Web data. A simulation has been created with the academic website crawled data. The simulation is done in JAVA and ORACLE environment. Results show that prediction of user session could give us plenty of vital information for the Business Intelligence. Search Engine Optimization could also use these potential results which are discussed in the paper in detail.

Keywords

Web Log, Mining, Clustering, Social Network Analysis.

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